Journal Article

Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

McGinnis, Karen, Huang, Ji, Zheng, Juefei, Yuan, Hui

2018

Overview

Background Transcription factors (TFs) are proteins that can bind to DNA sequences and regulate gene expression. Many TFs are master regulators in cells that contribute to tissue-specific and cell-type-specific gene expression patterns in eukaryotes. Maize has been a model organism for over one hundred years, but little is known about its tissue-specific gene regulation through TFs. In this study, we used a network approach to elucidate gene regulatory networks (GRNs) in four tissues (leaf, root, SAM and seed) in maize. We utilized GENIE3, a machine-learning algorithm combined with large quantity of RNA-Seq expression data to construct four tissue-specific GRNs. Unlike some other techniques, this approach is not limited by high-quality Position Weighed Matrix (PWM), and can therefore predict GRNs for over 2000 TFs in maize. Results Although many TFs were expressed across multiple tissues, a multi-tiered analysis predicted tissue-specific regulatory functions for many transcription factors. Some well-studied TFs emerged within the four tissue-specific GRNs, and the GRN predictions matched expectations based upon published results for many of these examples. Our GRNs were also validated by ChIP-Seq datasets (KN1, FEA4 and O2). Key TFs were identified for each tissue and matched expectations for key regulators in each tissue, including GO enrichment and identity with known regulatory factors for that tissue. We also found functional modules in each network by clustering analysis with the MCL algorithm. Conclusions By combining publicly available genome-wide expression data and network analysis, we can uncover GRNs at tissue-level resolution in maize. Since ChIP-Seq and PWMs are still limited in several model organisms, our study provides a uniform platform that can be adapted to any species with genome-wide expression data to construct GRNs. We also present a publicly available database, maize tissue-specific GRN (mGRN, https://www.bio.fsu.edu/mcginnislab/mgrn/ ), for easy querying. All source code and data are available at Github ( https://github.com/timedreamer/maize_tissue-specific_GRN ).

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Agriculture / Algorithms / Arabidopsis / artificial intelligence / Bioinformatics / Biomedical and Life Sciences / Cluster analysis / Clustering / Corn / data collection / Data processing / Deoxyribonucleic acid / DNA / DNA methylation / Eukaryotes / eukaryotic cells / Gene expression / gene expression regulation / Gene Expression Regulation, Plant - genetics / Gene Expression Regulation, Plant - physiology / Gene regulation / gene regulatory networks / Gene Regulatory Networks - genetics / Gene Regulatory Networks - physiology / Gene sequencing / genes / Genes, Plant - genetics / Genes, Plant - physiology / Genetic aspects / Genetic transcription / Genomes / Genomics and evolution / Learning algorithms / leaves / Life Sciences / Machine learning / Maize / Mathematical models / Meristem - metabolism / Network / Network analysis / Nucleotide sequence / oxygen / Plant Leaves - metabolism / Plant Roots - metabolism / Plant Sciences / Plant Shoots - metabolism / prediction / Predictions / Proteins / Research Article / Ribonucleic acid / RNA / Seeds - metabolism / sequence analysis / Tissue analysis / Tissues / transcription (genetics) / Transcription factor / Transcription factors / Transcription Factors - genetics / Transcription Factors - physiology / Transcriptional regulation / Tree Biology / Zea mays / Zea mays - genetics / Zea mays - metabolism